Integrated Uncertainty Quantification by Probabilistic Forecasting Approach in the Field Development Project
Bibliographic record
Abstract
Abstract Development studies examine the importance of geologic, engineering, and economic parameters to formulate and optimize production plans. If there are many factors, these studies are high-priced unless simulation runs are chosen and analyzed efficiently. Reservoir studies require integration of geological properties of the reservoir, drilling and production strategies, and economic parameters. Integration is complex, because parameters such as permeability, drive mechanism, structural framework, and fluid saturation distributions are uncertain. Uncertainty in permeability, for example, could be caused by prediction at unknown locations from inexact seismic data, poorly distributed of precise well data and imprecise seismic data. Therefore the impact of uncertainty levels in key geologic and production parameters such as NTG, permeability, porosity, vertical transmissibility, skin factor must be assessed. The application of the system on a single, sector carbonate reservoir unit is taken through a development-plannig case study based on Field Development Plan, FDP. Ultimate recoveries, profiles, and economics for the range of possibilities are evaluated. Uncertainty quantification is attained in the development of a method that can model and quantify uncertainty in reservoir simulation in an efficient and practical way. In this study variety of approaches are investigated to estimate the uncertainty in a recovery prediction. The methodology which is employed in this study uses Monte Carlo simulation approach (probabilistic forecasting) and efficient selective simulation runs with simultaneous, multi variable input modifications. The results indicate that in terms of reserves points of view the main uncertainties are represented by NTG and permeability distribution. This can be attributed to indigenous heterogeneous carbonate reservoir deposition and dissolution. The uncertainity to the OOIP is also affected mainly by the reservoir structural framework; reservoir top depth variations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".